code: def test_handle_drastic_imbalance(self):
      a = AutoML(
          results_path=self.automl_dir,
          total_time_limit=10,
          algorithms=["Random Forest"],
          train_ensemble=False,
          validation_strategy={
              "validation_type": "kfold",
              "k_folds": 10,
              "shuffle": True,
              "stratify": True,
          },
          start_random_models=1,
      )
  
      rows = 100
      X = pd.DataFrame(
          {
              "f1": np.random.rand(rows),
              "f2": np.random.rand(rows),
              "f3": np.random.rand(rows),
          }
      )
      y = np.ones(rows)
  
      y[:8] = 0
      y[10:12] = 2
      y = pd.Series(np.array(y), name="target")
      a._ml_task = MULTICLASS_CLASSIFICATION
      a._handle_drastic_imbalance(X, y)
  
      self.assertEqual(X.shape[0], 130)
      self.assertEqual(X.shape[1], 3)
      self.assertEqual(y.shape[0], 130)
  
file_path: mljar-supervised/tests/tests_automl/test_handle_imbalance.py
imports: import os
  import unittest
  import tempfile
  import json
  import numpy
  import pandas
  import shutil
  from sklearn import datasets
  from supervised import AutoML
  from supervised.algorithms.registry import MULTICLASS_CLASSIFICATION
  from supervised.algorithms.random_forest import additional
  from supervised.algorithms.xgboost import additional
label_desc: Use image node_id 2 for calling the AutoMLHandleImbalanceTest obj's underlying member method code with example usage: obj.test_handle_drastic_imbalance() without return types